Due　to　the　changes　in　soil　moisture　content　and　deformation　caused　by　weather　changes,　there　is　a　correlation　between　weather　temperature　and　failures　of　urban　water　distribution　pipelines.　Based　on　the　failure　records　of　pipelines　and　weather　temperatures　of　a　northern　China　city,　the　correlations　between　different　weather　temperature　indicators　and　water　mains　failures　were　analyzed.　The　failure　prediction　models　of　water　distribution　pipelines　considering　weather　temperature　factors　were　established　using　error　back　propagation　neural　network　(BPNN)　and　genetic　expression　programming　(GEP)　methods.　According　to　the　failure　records　of　water　distribution　pipelines,　pipeline　geographic　information,　and　weather　temperature　records　of　the　case　city　in　the　past　11　years,　the　correlations　among　pipeline　failures　and　six　weather　factors　were　analyzed,　including　average　temperature,　freezing　indicator,　maximum　increase,　maximum　decrease,　maximum　increase　rate,　and　maximum　decrease　rate.　BPNN　and　GEP　were　used　to　establish　the　implicit　and　explicit　relationships　between　the　number　of　pipeline　failures　(i.e.,　the　dependent　variable)　and　four　explanatory　variables　(the　selected　weather　temperature　indicator,　the　diameter,　age,　and　length　of　pipelines).　The　explicit　and　implicit　models　were　used　to　predict　the　number　of　pipeline　failures　in　the　case　city　in　the　next　year.　The　determination　coefficients　of　the　prediction　results　of　the　model　without　considering　weather　factors　were　0.65　and　0.60　respectively,　while　those　considering　weather　factors　were　0.78　and　0.88,　where　the　prediction　accuracy　increased　by　13%　and　28%.　Therefore,　it　is　reasonable　and　effective　to　establish　a　failure　prediction　model　of　water　distribution　pipelines　by　considering　weather　factors.　Copyright　©2022　Journal　of　Harbin　Institute　of　Technology.All　rights　reserved.